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Field
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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. A background in forest engineering, ecohydrology, and/or ecology would be ideal but is not required. Experience in numerical modelling, big data analysis, and plant ecological and physiological
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numerical modelling skills (e.g. Python, MATLAB, CFD codes) Personal characteristics Flexible and dependable Collaborative and independent Innovative and open minded Strong analytical skills Emphasis will be
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). ABOUT YOU Applicants should hold a PhD (or equivalent) in synthetic biology, biomaterials, biofabrication, microbiology or biodesign. Being familiar with material-producing non-model microbes and co
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, control), physical and numerical modelling (2D/3D) Independent research in the field of the dissertation topic Independent teaching, language of instruction German Collaboration in various research projects
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently